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4,299,418 works, Canadian by any of four routes.

Every filter state is a URL; the URL is the query; the query is citable via /q/⟨hash⟩. The page, the API and the export parse the same parameters.

The current cohort, streamed from the database: every work column, the machine labels, the provisional scores, and the per-row validation status. Exports are capped at 100,000 rows. Mints a permanent /q/ link for this exact query. The same filters always produce the same link, whoever asks.

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Forecasting Techniques and Applications
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Direct Codex and Gemma labels are unvalidated and sparse. Distilled predictions cover the full frame and are also unvalidated. Choose the evidence source explicitly; absence of a direct label is never a negative label.

affaffiliation
fundfunder
venuejournal
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The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

497 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
497 works in the cohort · of 4,299,418page 1 of 10

Labels cover 0 of 497 works in this cohort. The rest are unlabeled, which is not a negative label: the label table is sparse today and grows as labeling rounds land.

Distilled predictions cover 497 of 497 works in this cohort. Predictions are machine_predicted_unvalidated. The Gemma side is a direct model label for every work (title-only); the Codex side is a distilled, calibrated classifier. Candidate is the union; consensus is the intersection.

fundno affno abstractunlabeled
Principles of Forecasting
2001· book· en· International series in management science/operations research/International series in operations research & management science· Decision Sciences
machine prediction:candidate · noneconsensus · none
1,162
citations
afffundno abstractunlabeled
Justify your alpha
Daniël Lakens, Federico Adolfi, Casper J. Albers, Farid Anvari, Matthew A J Apps, Shlomo Argamon +82 more
2018· article· en· Nature Human Behaviour· Decision Sciences
machine prediction:candidate · metaresearchconsensus · none
492
citations
affunlabeled
The Operational Value of Social Media Information
Ruomeng Cui, Santiago Gallino, Antonio Moreno, Dennis Zhang
2017· article· en· Production and Operations Management· Decision Sciences
machine prediction:candidate · noneconsensus · none
389
citations
aboutno affunlabeled
Forecasting emergency medical service call arrival rates
David S. Matteson, Mathew W. McLean, Dawn B. Woodard, Shane G. Henderson
2011· article· en· The Annals of Applied Statistics· Decision Sciences
machine prediction:candidate · noneconsensus · none
123
citations
affunlabeled
Improving Intelligence Analysis With Decision Science
Mandeep K. Dhami, David R. Mandel, Barbara A. Mellers, Philip E. Tetlock
2015· review· en· Perspectives on Psychological Science· Decision Sciences
machine prediction:candidate · noneconsensus · none
110
citations
affaboutunlabeled
Machine Learning-Based Demand Forecasting in Supply Chains
Réal A. Carbonneau, Rustam Vahidov, Kevin Laframboise
2007· article· en· International Journal of Intelligent Information Technologies· Decision Sciences
machine prediction:candidate · noneconsensus · none
53
citations
venueno affunlabeled
Spurious Relationships for Nearly Non-Stationary Series
Yushan Cheng, Yongchang Hui, Michael McAleer, Wing‐Keung Wong
2021· article· en· Journal of risk and financial management· Decision Sciences
machine prediction:candidate · noneconsensus · none
31
citations
affunlabeled
Data Aggregation and Demand Prediction
Maxime C. Cohen, Renyu Zhang, Kevin Jiao
2022· article· en· Operations Research· Decision Sciences
machine prediction:candidate · noneconsensus · none
24
citations

How this was built: Screen · Findings · About